Brain tumor image classification is one of the predominant tasks of brain image processing. The three-class brain tumor classification becomes a trivial task for researchers as each tumor exhibit distinct characteristics. Existing classification models use deep neural networks and suffer from high computational cost. We have proposed an eight-layer average-pooling convolutional neural network to address three-class brain tumor classification. The proposed model uses three convolution blocks along with a dense layer and a softmax layer. We have utilized N-adam optimizer with a sparse-categorical cross-entropy loss function to improve the learning rate. The proposed model has been evaluated using a dataset consists of 3064 brain tumor magnetic resonance images. The proposed model outperforms state-of-the-art models with 97.42% accuracy and takes lesser computation time than its competitive models.

中文翻译：

---

使用平均池化卷积神经网络对脑磁共振图像进行三类分类

脑肿瘤图像分类是脑图像处理的主要任务之一。由于每个肿瘤表现出不同的特征，三类脑肿瘤分类成为研究人员的一项微不足道的任务。现有的分类模型使用深度神经网络，计算成本高。我们提出了一个八层平均池化卷积神经网络来解决三类脑肿瘤分类问题。所提出的模型使用三个卷积块以及一个密集层和一个 softmax 层。我们使用了带有稀疏分类交叉熵损失函数的 N-adam 优化器来提高学习率。已使用由 3064 个脑肿瘤磁共振图像组成的数据集对所提出的模型进行了评估。所提出的模型以 97 的成绩优于最先进的模型。